1. Field of the Invention
The present invention relates to an object identification device to identify objects existing in an image capturing area, a moving object controlling apparatus to conduct a movement control of moving vehicles such as automobiles, ships, airplanes, and industrial robots using an identification result of the object identification device, and an information presenting apparatus to present useful information to operators of the moving vehicles. Further, the present invention relates to a spectroscopic image capturing apparatus to obtain a two-dimensional spectroscopic image, in which wavelength information is correlated to each point in an image capturing area.
2. Description of the Background Art
Object identification devices are widely employed for moving object control apparatuses to control moving vehicles such as automobiles, ships, airplanes, and industrial robots, and for information presenting apparatuses to present useful information to operators of moving vehicles. Specifically, for example, the object identification devices are employed for a driver support system such as adaptive cruise control (ACC) to reduce burden on operators of vehicles.
Such vehicle driving support systems implement an automatic braking and warning to evade obstacles and to reduce the shock of impact in the event of a collision; vehicle speed adjustment to maintain the vehicle-to-vehicle distance with a vehicle ahead; and driving assist to prevent a vehicle from straying out of its lane. Such vehicle driving support systems must be able to effectively differentiate, and recognize or identify, objects such as obstacles existing around one vehicle, another vehicle in front of one vehicle (in-front vehicle, hereinafter), lane markings, and the like.
Hitherto, several object identification devices have been disclosed. For example, JP-H11-175702-A discloses an object identification device that identifies objects such as lane markings, in which lines in a captured image of roads are detected to detect a change in relative position of a vehicle such as an automobile with respect to traffic lanes as defined by lane markings such as white-painted lines or white lines.
In general, when puddles of water are present on a road due to rainy weather, sunlight reflected off the puddles as specular reflection may be captured with a same light intensity as that of the lane markings (e.g., the white lines) on the road and misidentified as lane markings. The object identification device is used to solve such problem.
Specifically, the effect of puddles is removed from the road image before conducting white line identification processing by removing a specular reflection component (i.e., noise component) from the captured road image, after which the white lines are recognized using the scattered light component. The specular reflection component can be removed by taking advantage of the fact that the horizontal polarized component of the specular reflection has a Brewster's angle of substantially zero, and the scattered light component has substantially the same level of vertical polarized component and horizontal polarized component. A difference between the vertical polarized component and the horizontal polarized component included in the captured road image is computed and a correction coefficient dependent on an angle of incidence included in the horizontal polarized component is multiplied by the computed difference to a compute specular reflection component. The computed specular reflection component is then subtracted from the horizontal polarized component to obtain a scattered light component image from which only the specular reflection component is removed from the road image.
JP-2004-299443-A discloses a road condition detector to detect whether a road surface is wet or dry. A captured image of the road surface includes a vertical polarized component of a vertical polarized image and a horizontal polarized component of a horizontal polarized image. A ratio of the polarized components is computed as a polarized light intensity ratio. Based on the computed polarized light intensity ratio, the road condition detector detects a road surface condition. The road condition detector uses a moving average method to reduce the effect of changes in ambient lighting occurring while the vehicle is moving, the effect of noise caused by the installation angle of a vehicle-mounted camera to compute a polarized light intensity ratio can be computed by reducing noise.
Similar to JP-2004-299443-A, JP-2009-25198-A discloses a road condition detector to detect whether a road surface is wet or dry, in which a noise removing device removes noise caused by incident light intermittently emitted by streetlamps/streetlights. The road condition detector can obtain road surface images having less effect of the streetlamps/streetlights. Based on a ratio of vertical polarized components and horizontal polarized components of the obtained images and a difference between vertical polarized components and horizontal polarized components of the obtained images, the road condition detector can detect whether the road surface is wet or dry.
Conventional object identification devices identify identification target objects, such as obstacles on road surfaces, in a captured image area using a difference in light intensity observed in the captured image area. Specifically, the object identification device extracts boundaries or edges of an identification target object and then identifies the identification target object, defined by the edges, in the captured image area. The identification target objects may be road side-end obstacles such as side walls, guardrails/crash barriers, telegraph poles/utility poles, streetlamps/streetlights, stepped portions such as pedestrian crossings at the road side-end, in-front vehicles, lane markings, or the like.
However, the above-described method may have a problem if the captured image includes a noise component, which degrades the precision of light intensity data used for identifying objects. For example, if no objects such as lane markings exist on a road surface but a high light intensity portion of road surface having a light intensity too great compared to other portions of the road surface exists on the road surface, the object identification device may extract boundaries of such portions as edges of an object, and then misidentify such portion of the road surface as an object existing on the road surface such as lane markings. When such misidentification occurs, the adaptive cruise control (ACC) misidentifies a shaded portion at a road side-end as an obstacle such as a side wall and initiates erroneous control or erroneous processing in the form of an evasive maneuver or the like.
JP-2011-150688-A discloses a method of identifying three-dimensional objects on a road surface. Each of two polarized images captured by an image capturing device is divided into a plurality of processing areas. For each processing area, a total light intensity of the two polarized images and a difference in light intensity between the two polarized images are computed, and then a polarization intensity difference, which is a ratio of the difference of light intensity with respect to the total light intensity, is computed to identify three-dimensional objects on the road surface. Specifically, based on the computed polarization intensity difference, the processing areas corresponding to the identification target object are determined, and a plurality of processing areas, which are close to each other and determined as the processing areas corresponding to the identification target object, are identified as an image area of identification target object.
In the conventional method, when a difference of light intensity in a captured image is not so great, the identification precision may deteriorate. The method in described in JP-2011-150688-A can identify three-dimensional objects in a captured image with a high precision even if the difference of light intensity in the captured image is not so great.
An object identification method using a difference in light intensity in the captured image and the three-dimensional object identification method using the polarization intensity difference may not identify an image area of identification target object in a captured image with high precision if some light intensity information such as noise component exists in the captured image, because such noise component degrades the precision of identification of the target object. Such noise component can be removed from captured images using conventional noise removing methods using noise removing parameters, and the object identification processing is conducted using captured images after conducting the noise removing processing on the captured images.
However, conventional noise removing methods cannot remove noise components from captured images effectively when environmental conditions of objects in image capturing areas differs such as fine weather vs. rainy weather, sunny place vs. shade place, difference of light angle irradiated on an object, or the like. Such problem occurs not only for the object identification devices for the driver support system, but also for the object identification devices used for other fields such as robot control.
Further, spectroscopes are widely used to obtain an optical spectrum. Typical, spectroscopes use a prism or a diffraction grating to decompose the incident light into a plurality of wavelength components, and the light intensity of wavelength components is detected by a light receiving element. However, the typical spectroscope cannot correlate positional information of incident light with the optical spectrum of incident light.
Recently, spectroscopic image capturing apparatuses that can capture a two-dimensional spectroscopic image have been developed, in which each point in an image capturing area can be correlated with the wavelength of the light measured at each point. Such spectroscopic image expresses the two-dimensional distribution of wavelengths at each point in the image capturing area, and the wavelength components at each point can be expressed, for example, as a difference in gradation in the image capturing area. The optical spectrum can be obtained with a wavelength selection filter such as a band pass filter and a low pass/high pass filter, by a dispersive element such as a prism and diffraction grating, and by the Fourier-transform spectroscopy. Spectroscopic image capturing apparatuses using such methods to obtain the optical spectrum have been developed.
Spectroscopic image capturing apparatuses using such methods to obtain the optical spectrum have been developed. For example, JP-2005-57541-A discloses a spectroscopy camera head unit using a wavelength selection filter to capture a spectroscopic image. In the spectroscopy camera head unit, a two-dimensional image capturing element receives incident light from a photographic subject via the wavelength selection filter, and a spectroscopic image of wavelength components matched to the wavelength selection filter is obtained. The spectroscopy camera head unit uses a liquid crystal wavelength tunable filter (LCTF) as the wavelength selection filter to dynamically switch wavelength that can pass the filter. Therefore, by capturing images while switching passable wavelength of the wavelength selection filter, a plurality of images captured using different wavelength components can be obtained. By synthesizing such images, a two-dimensional spectroscopic image that correlates each point in an image capturing area with wavelength components of light measured at each point can be obtained.
A non-patent reference (pages 4-9, November 1999, “Optical Alliance”, JAPAN INDUSTRIAL PUBLISHING CO., LTD) discloses a planar spectrometric system using a dispersive element that can capture a spectroscopic image. The planar spectrometric system employs imaging spectroscopy that simultaneously measures positional information and spectrum information at multiple points arranged in a straight line to obtain a spectroscopic image. The imaging spectroscopy captures images by scanning in a direction perpendicular to the arrangement direction of the multiple points, by which a two-dimensional spectroscopic image, which correlates each point in an image capturing area with wavelength of light measured at each point can be obtained.
JP-2005-31007-A discloses a spectral instrument using Fourier-transform spectroscopy. In this spectral instrument, the incident light is split into two light paths or two polarized components and one of the light paths or polarized components is given a certain phase difference that causes the two light paths or two polarized components to interfere with each other to generate detection signals. The detection signals are Fourier-transformed by a computer to obtain an optical spectrum. The spectral instrument can obtain a spectroscopic image by conducting detection while changing the phase difference set for the two light paths or two polarized components, perpendicular to each other, by which a two-dimensionally distributed optical spectrum for a given wavelength range can be obtained.
However, the above methods need a long processing time to obtain the spectroscopic image, and therefore such methods may not be suitable to capturing the spectroscopic image in real time at high speed. Specifically, when the wavelength selection filter is used, one image-capturing action can obtain a two-dimensional spectroscopic profile only for one wavelength component. Therefore, to obtain a two-dimensional spectroscopic image for a plurality of wavelengths, a plurality of images for different wavelength components needs to be captured and then synthesized. These operations take time, thereby lengthening the processing time required for obtaining the spectroscopic image. Such lengthening of the processing time may occur similarly when the dispersive element and Fourier-transform spectroscopy are used.
In general, spectroscopic images generated by using the image difference captured by a spectroscopic image capturing apparatus include a noise component. The difference between any two adjacent areas in the spectroscopic image can be expressed as a difference in gradient. The received light intensity of the two adjacent areas is measured and compared. If the received light intensity in one of the adjacent areas becomes too great compared to the received light intensity in the other one of the adjacent areas, such difference can be identified as noise because such a great difference does not usually occur in adjacent areas, which receive light at adjacent points in an image capturing area. If such noise information is included in a spectroscopic image, the spectroscopic image may not be processed correctly subsequently. For example, when the object identification process to recognize edge portions in the spectroscopic image as a contour of object is conducted, the noise in the spectroscopic image may be extracted as an edge portion of object, thereby degrading object identification precision.